Solbert
Solbert computes prime-implicant explanations for scikit-learn decision trees and random forests. These explanations are also known as abductive explanations or sufficient reasons: inclusion-minimal sets of feature conditions that are enough to force a classification result.
Solbert translates a trained classifier into a propositional formula, uses the incremental SAT solver CaDiCaL through its IPASIR interface, and decodes the resulting prime implicants back into feature-space case distinctions.
Installation
Install a prebuilt wheel from PyPI:
python -m pip install solbert
Wheels are built for supported CPython versions on Linux (x86-64 and AArch64) and macOS (Apple Silicon).
Package Structure
solbertprovides native prime-implicant and projected-model operations.solbert.treeprovides decision-tree wrappers, encoders, and explainers.solbert.forestprovides random-forest wrappers, encoders, and explainers.solbert._nativeis the private C++ extension backed by CaDiCaL.
The Python encoders represent split thresholds, tree nodes, and classification outcomes as a CNF resembling a monotonic circuit. Prime implicants of that encoding are minimized incrementally and decoded into minimal case distinctions over the original features.
Development
Install directly from a checkout:
python -m pip install ./solbert
The source build requires a C++17 compiler, CMake, Git, and Make. It downloads a pinned CaDiCaL release automatically. Local CaDiCaL builds use one job by default; a stronger machine can opt into parallel compilation:
CMAKE_ARGS="-DSOLBERT_CADICAL_BUILD_JOBS=4" python -m pip install ./solbert
To reuse an existing position-independent CaDiCaL archive:
CMAKE_ARGS="-DSOLBERT_CADICAL_LIBRARY=/path/to/libcadical.a" \
python -m pip install ./solbert
To build and run directly from the checkout without installing:
cmake -S solbert -B solbert/build
cmake --build solbert/build --target _native
python solbert/eval.py 1
CMake builds copy the native extension into the source package. The generated extension is ignored by Git.
Run the package tests with:
python solbert/test/test_solbert.py
Research
Solbert implements the method described in Calculating Sufficient Reasons for Random Forest Classifiers by Ashlin Iser. The paper source contains the formal encodings, prime-implicant algorithm, and evaluation on SAT benchmark classification and solver selection.
If you use Solbert in research, please cite that paper and link to this repository.
1 maintainer
Metadata
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Release files for solbert 1.0.2
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Source distribution (sdist)
| File | Size | Uploaded | |
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| solbert-1.0.2.tar.gz | 28.7 kB | Details |
Built distributions (wheels)
Total release size: 86.7 MB
Release files / solbert-1.0.2.tar.gz
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